Management of patients with chronic disease consumes a significant proportion of the total health care expenditure in the United States. Many of these diseases are widely prevalent and have significant annual incidences as well. Heart failure prevalence alone is estimated at over 5.5 million patients in 2000 with incidence rates of over half a million additional patients annually, resulting in a total health care burden in excess of $20 billion. Heart failure, like many other chronic diseases such as asthma, chronic obstructive pulmonary disease (“COPD”), chronic pain, and epilepsy is event driven, where acute episodes of disease result in hospitalization. In addition to causing considerable physical and emotional trauma to the patient and family, event driven hospitalizations consume a majority of the total health care expenditure allocated to the treatment of heart failure.
An interesting fact about the treatment of acute episodes of disease is that hospitalization and treatment occurs after the acute event has happened. However, most Heart Failure patients exhibit prior non-traumatic symptoms, such as steady weight gain, in the weeks or days prior to the acute episode. If the physician is made aware of these symptoms, it is possible to intervene before the event, at substantially less cost to the patient and the health care system.
Intervention before the event is usually in the form of a re-titration the patient's drug cocktail, reinforcement of the patient's compliance with the prescribed drug regimen, or acute changes to the patient's diet and exercise. Such intervention is usually effective in preventing the acute episode and thus avoiding hospitalization. NYHA Class III and late Class II HF patients often have acute episodes three or four times annually, each episode resulting in hospital stays of three or four days.
However, many acute episodes of disease can be predicted by analyzing biometric trends. Predictive accuracy may be improved by analyzing such biometric trends in view of clinically derived algorithms. In practice, the algorithmic analysis of contemporaneous biometric information or data in reference to a temporal event can report and assist in the identification of a state of patient health or disease progression. Yet, data collection and rapid analysis is a limiting factor in effectively using clinical algorithms to report such states of patient health.
Thus, for these and other reasons, there is a need for a system and method for efficiently and effectively reporting a state of patient health or disease progression by correlating biometric information or trends with a related temporal event and alerting the patient or physician of the state of patient health or disease progression.